Askincorp Inc

Note: Only candidates who have recently worked on AI projects and have hands-on coding experience are eligible to apply. Key Responsibilities Lead the architecture, design, and deployment of scalable data solutions using Databricks and the medallion architecture. Guide technical teams in building batch and streaming data pipelines using Spark, Delta Lake, and MLflow. Collaborate with clients and internal stakeholders to understand business needs and translate them into robust data and AI architectures. Design and prototype Generative AI applications using LLMs, RAG pipelines, and vector stores. Provide thought leadership on the adoption of Agentic AI systems in enterprise environments. Mentor data engineers and solution architects across multiple projects. Ensure adherence to security, governance, performance, and reliability best practices. Stay current with emerging trends in data engineering, MLOps, GenAI, and agent-based systems. Technical Skills Databricks (incl. Spark, Delta Lake, MLflow, Unity Catalog) Python, SQL, PySpark GenAI tools and libraries (LangChain, OpenAI, etc.) CI/CD and DevOps for data REST APIs, JSON, data serialization formats Cloud services (Azure/AWS/GCP) Soft Skills Strong communication and stakeholder management skills Ability to lead and mentor diverse technical teams Strategic thinking with a bias for action Comfortable with ambiguity and iterative development Client-first mindset and consultative approach Excellent problem-solving and analytical skills Preferred Certifications Databricks Certified Data Engineer / Architect Cloud certifications (Azure/AWS/GCP) Any certifications in AI/ML, NLP, or GenAI frameworks are a plus Qualifications Bachelor's or Master's degree in Computer Science, Engineering, or related technical discipline. 10+ years of experience in data architecture, data engineering, or software architecture roles. 5+ years of hands-on experience with Databricks, including Spark SQL, Delta Lake, Unity Catalog, and MLflow. Proven experience in designing and delivering production-grade data platforms and pipelines. Exposure to LLM frameworks (OpenAI, Hugging Face, LangChain, etc.) and vector databases (FAISS, Weaviate, etc.). Strong understanding of cloud platforms (Azure, AWS, or GCP), particularly in the context of Databricks deployment. Knowledge or interest in Agentic AI frameworks and multi-agent system design is highly desirable.